Control of unmanned agricultural vehicles using neural network-based control system

Recent advances in the fields of robotics, control systems, and wireless communication technologies have accelerated the studies related with unmanned agricultural vehicles. The unmanned agricultural vehicles have been developed to perform various functions in agricultural fields such as seeding, spraying, hoeing, weed and pest control, and harvest. Given the reduction in the number of people working in agriculture and industry, it is obvious that intelligent unmanned agricultural vehicles will be an indispensable part of agriculture in the future. In this paper, PID controller and model-based neural network PID control system has been applied for controlling an unmanned agricultural vehicle. To test the performance of control structures developed, three different input signals were used. When the results of both control structures were examined in the way of transient-state response (such as rise time, settling time, peak time, peak value, settling time) and steady-state response, the control system produced had favorable results.